Search Results (38,348)

This study combined the damage effect of UV and the repair effect of He-Ne laser, employing UV-He-Ne laser (UV-LA) sequential mutagenesis to improve the protease-producing ability of Bacillus velezensis. Compared with UV mutagenesis, UV-LA treatment elevated the positive mutation rate from 12.50% to 16.67%, and the enzyme activity of the mutant strain increased from 20.81 U/mL to 34.04 U/mL. Following 20 passages, the degree of enzyme production decline of the UV-LA strain was reduced from 44% to 23% compared with that of the UV strain, showing better genetic stability. Subsequent investigation revealed that the physiological activity (L-LDH, ATP content) and the activities of various key metabolic enzymes (PFK, IDH, ATPase) in the UV-LA strain exceeded those in the UV strain. Genome sequencing results showed that the UV-LA strain had fewer mutations than the UV strain, and the mutation sites were more concentrated in the regulatory region. Proteomic analysis identified 108 differentially expressed proteins (56 upregulated and 52 downregulated) between the UV strain and UV-LA strain, primarily associated with bacterial energy metabolism, translation, and secretion systems. Research indicates that UV-LA treatment may leverage the laser’s light effects to reduce UV damage, thereby achieving regulation of metabolic and secretory processes. This study is expected to develop a novel and efficient mutagenesis method for microbial breeding and to offer new ideas for expanding the application of UV mutagenesis technology. Full article

The liver’s regenerative capacity is essential for resection and transplantation, but chronic liver diseases compromise this ability. Conventional preoperative tests poorly predict regeneration in diseased livers, necessitating new intraoperative tools. Optical coherence tomography (OCT) provides real-time high-resolution “optical biopsy” and functional tissue assessment, making it a promising method for evaluating liver regenerative potential. The aim of this study was to identify characteristic criteria derived from MM OCT that could be used intraoperatively to detect the presence and type of pathology, and to assess the reduction in liver regenerative potential in patients. In this study, we used intraoperative multimodal OCT (MM OCT)—combining attenuation mapping and elastography—for real-time, label-free assessment of changes in liver structure, and stiffness during regeneration. MM OCT also provides higher resolution and specificity than conventional clinical imaging. In a rat model, we induced steatosis by high-fat diet and fibrosis by CCl₄ injections, then induced regeneration by 70% partial hepatectomy (PH). MM OCT monitoring was performed on day 0 (pre-PH), day 3, and day 7 (post-PH). Pathologies and regeneration were verified by biochemical blood tests, histological analysis, and real time-PCR for steatosis- and fibrosis-specific genes. As a result, attenuation coefficient and Young’s modulus obtained by MM OCT enable real-time assessment of liver tissue changes in steatosis and fibrosis, which vary with regeneration stage. Steatosis showed uniformly high attenuation and low stiffness before and after resection. Fibrosis exhibited heterogeneous attenuation and marked stiffness fluctuations—high pre-resection values dropped at day 3 and rose again by day 7. Attenuation coefficient detects lipid droplets in hepatocytes and identifies high-density hepatocyte zones near collagen septa, distinguishing them from densely packed unaltered cells. Thus, MM OCT identifies pathology type, lipid infiltration and collagen presence—features that serve as indicators of impaired liver regenerative capacity. Full article

Natural lithium-bearing brines are gaining strategic importance as an alternative to traditional hard-rock deposits. Approximately 78% of the identified global lithium resources are hosted in hydromineral environments, including continental brines, oilfield formation waters, and geothermal fluids. The Shu–Sarysu sedimentary basin in southern Kazakhstan is one of the most extensive, yet poorly studied, brine provinces in Central Asia. This study provides a comprehensive hydrogeochemical characterization of lithium-bearing brines in the Moiynkum structural zone of the Shu–Sarysu basin, based on regional field sampling, multielement analysis, and GIS data integration. Water samples were collected from four deep gas production wells (perforation depths of 2029–2290 m) at the Ayrakty and Amangeldy fields. Analytical data demonstrate highly concentrated chloride–calcium–sodium brines with total dissolved constituent concentrations (TDS, calculated as the sum of analyzed ions) ranging from 140.1 to 272.1 g/L, with lithium content of 24.46–55.11 mg/L, strontium 680.5–1648.2 mg/L, rubidium 4.31–8.42 mg/L and cesium 0.317–0.420 mg/L. Piper and Durov diagrams classify the samples as highly evolved Na–Ca–Cl to Ca–Na–Cl formation brines typical of deep, long-residence sedimentary formation waters. Lithium enrichment is interpreted to reflect the combined influence of several processes: water–rock leaching of Li-bearing lithologies, evaporative concentration of ancestral brines, clay-mineral ion exchange, and possible deep fluid contributions, whose relative roles remain to be constrained by isotopic data. The compiled GIS-integrated database, combining new analytical data with archival hydrogeochemical records, delineates two promising lithium-bearing provinces and identifies priority areas for further exploration. The results indicate that the Shu–Sarysu Basin is a prospective region for further exploration of lithium-bearing formation waters in Kazakhstan. The recorded Li concentrations fall within the lower range of sedimentary-basin brines currently being evaluated for lithium extraction, although their economic and technological feasibility remains to be established. Full article

Background/Objectives: 22q11.2 microduplication syndrome is a rare genetic disorder characterized by the presence of one or two additional copies of a segment within the 22q11.2 region of chromosome 22. While much of the literature has focused on the deletion variant leading to DiGeorge syndrome, the duplication counterpart has gained increasing attention due to its clinical variability and under-recognition. This review aims to deliver new possibilities to genetic counseling that can be provided in prenatal and postnatal cases as the phenotype of 22q11.2 microduplication carriers cannot be fully predicted. Methods: In the present study, a total of 12 (5 prenatal and 7 postnatal) cases were diagnosed through array-CGH and combined with 679 (95 prenatal and 584 postnatal) cases reported in the literature. This review summarizes the published evidence available up to April 2025. Data on clinical presentations, genetic findings, diagnostic methodologies, and outcomes were extracted and analyzed. Results: The combination of our cases and the reported cases with 22q11.2 microduplication syndrome revealed a broad phenotypic spectrum. Common clinical features include neurodevelopmental disorders, and cardiac anomalies. Importantly, the syndrome exhibits variable expressivity and reduced penetrance, with more than 70% of the findings to be inherited by one of the parents. Conclusions: 22q11.2 microduplication syndrome presents a heterogeneous clinical picture with variable expressivity and incomplete penetrance, posing challenges in diagnosis and genetic counseling, particularly when predicting prenatal outcomes. Awareness of its diverse manifestations is crucial for clinicians to consider this syndrome in the differential diagnosis and to provide informed counseling. Full article

Cystoseira sensu lato (s.l.) (Fucales, Phaeophyceae) form structurally complex marine forests along the warm-temperate coasts of the Mediterranean Sea and the eastern Atlantic Ocean. They are experiencing widespread decline, yet their conservation and restoration are hindered by taxonomic uncertainty. Within Cystoseira sensu stricto (s.s.), species boundaries among taxa traditionally referred to as C. compressa, C. foeniculacea, C. humilis (including the C. canariensis morphotype), and C. pustulata have long been debated due to the high plasticity and partial overlap of diagnostic morphological traits. Here we reassess species boundaries within the Cystoseira s.s. complex using an integrative approach combining morphology, ecology, and multilocus genetic data. We collected specimens from 20 sites spanning the Mediterranean–Atlantic distribution. Genomic DNA was extracted from 220 specimens and Sanger sequencing generated 243 new sequences (ITS2: n = 99; cox1: n = 88; rbcL–rbcS: n = 56). We inferred single-locus and concatenated phylogenies, reconstructed haplotype networks, and applied multiple single-locus species delimitation approaches to test the robustness of inferred boundaries. We consistently recovered two primary lineages corresponding to C. foeniculacea and a C. compressa complex, the latter including specimens historically identified as C. pustulata and the C. humilis with the C. canariensis morphotype. Within an integrative framework that prioritises cross-locus concordance and diagnosability, we recognise two species-level lineages in Cystoseira s.s. and treat the morpho-ecologically coherent entities within C. compressa at the variety rank. Accordingly, we propose the new status and combination Cystoseira compressa var. pustulata (Ercegović) D’Ambros Burchio & Falace, comb. et stat. nov. Full article

Cannabidiol (CBD)-infused bottled water has recently become one of the new types of functional beverages in the expanding cannabinoid and nutraceutical market. This paper presents preliminary experimental observations combined with a literature-based analysis to investigate quality and stability parameters of CBD-infused bottled water. Physicochemical characterization, microbial quality analysis and stability monitoring of CBD under storage conditions were included in the experimental work. The results indicated that the formulation maintained the properties of its initial dispersion during the first period of storage. An increase in transparency and a decrease in CBD concentration were noted. Microbial counts increased during storage, suggesting that microbiological stability is limited by time under the tested conditions. Reviewing the literature underlines the significance of cannabinoid stability in aqueous beverages aided by nanoemulsion delivery systems, packaging materials and controlled storage environments. The results show that CBD can be infused into bottled water but challenges regarding formulation and storage exist. The research was not conducted to address long shelf-life, commercial scalability or compliance with regulations but to provide primary information on quality and stability factors influencing CBD beverages. More research using standardized procedures is required to understand safety, efficacy and stability over time. Full article

This study aims to accurately predict students’ academic performance trajectories for university entrance examinations by proposing a machine learning framework that explicitly accounts for missing data and uncertainty. Mock examination data are characterized by substantial missing values due to heterogeneous participation in exams, as well as inherent randomness caused by variations in test content and examinee conditions. Conventional single-value imputation methods cannot adequately reconstruct the missing values arising from such heterogeneous participation without introducing strong bias, and existing educational prediction models based on deterministic formulations do not account for the inherent randomness and uncertainty in examination scores, thereby limiting the reliability of their forecasts. To address these challenges, we employ GP-VAE and SAITS, state-of-the-art methods for time-series imputation, to reconstruct incomplete mock examination data. Furthermore, we develop a Bayesian Neural Network (BayesNN) to predict future academic performance while explicitly modeling uncertainty. By integrating temporally aware imputation with probabilistic prediction, the proposed framework aims to provide more accurate and reliable performance forecasts than existing approaches. We evaluate the effectiveness of the proposed method through comparative experiments involving various combinations of imputation techniques and prediction models. Experimental results demonstrate that the proposed framework achieves competitive predictive accuracy: the combination of deep imputation methods and BayesNN yields the lowest average estimation error of 15.98 points, compared with 16.75 points for the conventional combination of mean imputation and linear regression. The contribution of this study does not lie in proposing a new deep learning model itself, but rather in systematically comparing combinations of time-series imputation methods and uncertainty-aware prediction models using real-world mock examination sequence data with missing values, thereby providing effective design guidelines for educational data analysis. Full article

The energy transition and global decarbonization goals have accelerated the development of offshore renewable energy technologies, particularly in deep-water regions, where fixed foundations are limited by technical and economic constraints. Floating structures offer new opportunities for harnessing marine renewable resources, allowing them to be deployed in areas with favorable wind, wave, and oceanographic conditions. This paper presents a comprehensive analysis of European floating structures intended for hybrid renewable energy applications, combining environmental assessment, structural characteristics, hydrodynamic behavior, and energy integration aspects. Unlike previous analyses, which focused primarily on individual technologies, this study offers an integrated perspective on floating platform concepts—including spar, semi-submersible, tension-leg, barge, and FPSO-based solutions—as well as their potential for hybrid energy systems. The analysis shows that platform stability, motion response, and structural adaptability are critical factors affecting energy performance and operational reliability. Furthermore, the analysis highlights that hybrid configurations combining offshore wind, wave, and solar energy with energy storage technologies represent promising pathways toward more autonomous and sustainable offshore infrastructure. Key challenges related to design optimization, environmental loads, and system integration are also identified to support future developments in European offshore renewable energy. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder worldwide, affecting approximately 56.9 million people in 2021 and projected to reach 152 million by 2050. Its defining pathological features, amyloid-beta plaques and neurofibrillary tangles, accumulate for up to two decades before cognitive symptoms emerge, placing the preclinical and mild cognitive impairment (MCI) stages at the centre of the early detection problem. Despite this, current diagnostic practice in routine clinical settings remains unreliable, with post-mortem studies placing the specificity of clinical AD diagnosis between 44.3 and 70.8% even in specialist memory clinics. Machine learning (ML) and deep learning (DL) applied to neuroimaging and electrophysiological data have emerged as candidate tools for closing this diagnostic gap, yet whether the accuracy figures reported in published studies translate into clinically useful performance on independent data remains unresolved. This study presents a structured comparative review of machine learning and deep learning methods reported across four publicly available Alzheimer’s disease datasets, namely the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Open Access Series of Imaging Studies (OASIS), the OpenNeuro ds004504 electroencephalography (EEG) dataset, and the Kaggle Alzheimer’s magnetic resonance imaging (MRI) dataset. Thirteen model families are examined through the published literature rather than through new experiments, and for each model and dataset combination, the best accuracy reported in the source study is recorded alongside the model’s mathematical formulation. All performance figures reported in this abstract and throughout the paper are taken from the published studies reviewed, not from new experiments conducted by the authors. Across the reviewed studies, deep learning architectures pre-trained on ImageNet and fine-tuned on neuroimaging data are reported to produce the highest accuracy on MRI classification tasks. Residual Network (ResNet)-101 is reported at 98.21 percent on ADNI and 97.45 percent on OASIS, while the IncepRes fusion architecture reaches 98.35% on OASIS by combining multi-scale feature extraction from InceptionV3 with residual connectivity from ResNet152V2. Traditional machine learning classifiers remain competitive on tabular clinical and biomarker data, with Extreme Gradient Boosting (XGBoost) reaching 91% on ADNI multiclass features. Logistic Regression achieves 82 to 85% on binary ADNI classification and is the only classifier in this review that provides explicit per-feature prediction contributions without post hoc tooling. Gaussian Naïve Bayes achieves 80 to 83% on the same task. On the OpenNeuro EEG dataset, K-nearest neighbours (KNN) with singular value decomposition (SVD) entropy features achieves 91% binary accuracy, with feature engineering quality determining performance more reliably than classifier architecture. Eight principal findings emerge from the cross-dataset analysis. Binary classification consistently outperforms multiclass by 10 to 30% across all datasets, reflecting the genuine biological ambiguity of the mild cognitive impairment category. Dataset size and augmentation predict reported accuracy more reliably than model architecture. Ensemble methods outperform individual classifiers by 5 to 8% in nearly every imaging study. Deeper architectures can overfit small clinical cohorts. EEG models trail MRI models by approximately 10 to 15% on comparable binary tasks. Cross-dataset generalisation has not been systematically evaluated in most studies, and the few that have tested it report accuracy drops of 5 to 10% or more when models encounter data from different scanners or cohorts. Eight recurring limitations constrain the clinical utility of these findings. Small sample sizes and limited demographic diversity, severe class imbalance inflating raw accuracy metrics, poor cross-dataset generalisation driven by scanner heterogeneity, limited deep learning interpretability, the dominance of binary over multiclass tasks, the absence of longitudinal modelling despite available datasets, inadequate standardisation of preprocessing and evaluation protocols, and the signal-to-noise ratio constraints specific to EEG recordings of elderly patients collectively define the gap between benchmark performance and clinical readiness. Future work must prioritise multi-centre training cohorts, multimodal fusion architectures, longitudinal progression modelling, and standardised interpretability evaluation as non-optional requirements for any system intended for clinical deployment. Full article

As organizations lean more heavily on their IT systems, managing cyber risk is gaining increasing importance. Organizations are often challenged to determine which cybersecurity risk framework they should adopt. Choosing the right framework can have a significant impact on the quality of governance, operational resilience, and assurance in risk reporting. However, most prevalent cybersecurity risk frameworks vary significantly in their intent, design, and analytical approach. This makes it difficult for organizations to understand how each framework may meet their business needs. This study presents an AI-enhanced multi-criteria decision support approach for evaluating cybersecurity risk frameworks. The model incorporates machine learning-driven risk scoring as a conceptual input layer, enhancing the objectivity and analytical rigor of the comparison without executing new predictive algorithms. The methodology includes a hybrid approach of literature review, document analysis, and multi-criteria decision analysis (MCDA) to compare and rank NIST CSF, ISO 27001, FAIR, OCTAVE, and CRAMM based on eight criteria that are designed to represent modern requirements for risk frameworks, including governance, scalability, quantitative focus, and interoperability. These criteria also reflect differences in security metrics supported by each framework to provide an organized means to compare qualitative versus quantitative measurement methodologies. The results indicate that NIST CSF performs the best overall in agility, business alignment, and interoperability. ISO 27001 outperforms all others in established governance and compliance. FAIR outperforms all others in quantitative risk analysis and provides superior analytical depth that other frameworks do not offer. OCTAVE and CRAMM function well in legacy systems but lack scalability and are not well-suited for modern distributed systems. Robustness analysis shows that the ranking of NIST CSF, ISO 27001, and FAIR is consistent under different weighting combinations and industry types. The result of this research demonstrates that a combined or hybrid approach to cybersecurity risk framework selection, such as using NIST CSF with FAIR, can give organizations a more well-rounded foundation for applying machine learning-enabled risk analytics with cyber controls. This research also offers a reusable decision support tool that organizations can leverage when aligning their risk priorities to the features of cybersecurity risk frameworks. Full article

Oral administration of narrow therapeutic index anticoagulants like phenprocoumon (PHP) necessitates careful control of the kinetic release of the drug to avoid dose dumping and severe haemorrhagic effects. This study was carried out to prepare and characterize novel PHP delivery systems based on κ-carrageenan hydrogels, exploring the importance of potassium ion (K⁺) stabilization in controlling the release process. FT-IR, TG/DTG, and in vitro release studies were employed in combination with a new validated RP-HPLC assay. FT-IR and thermal analysis results showed that PHP is physically encapsulated into the polysaccharide matrix, where there are no chemical incompatibilities between them. Furthermore, potassium ions increase the stability and heat resistance of the polymer network. However, when K⁺ was considered for modelling the kinetic release using the Korsmeyer–Peppas equation, it was observed that PHP is released from the K⁺ stabilized matrix in a relaxation dominated diffusion-controlled transport. Ionic cross-linking effectively reduces the initial burst effect, demonstrating that these matrices are promising vehicles for the sustained delivery of phenprocoumon. Full article

Cutaneous co-infection with Mycobacterium tuberculosis (MTB) and Mycobacterium avium complex (MAC) is extremely rare and easily missed due to overlapping histopathological features. We report a previously healthy, HIV-negative middle-aged woman who presented with a progressive destructive mass in the left inguinal-perineal region. Imaging revealed sinus tract formation, osteolytic bone lesions, and chronic inflammation in the right middle lobe of the lung. Initial metagenomic next-generation sequencing (mNGS) detected 3756 reads of the Mycobacterium tuberculosis complex (MTBC) and 111 reads of Mycobacterium intracellulare (M. intracellulare); the latter was interpreted as possible colonization or contamination because of its low abundance. Empirical anti-tuberculosis therapy produced only transient partial improvement, followed by paradoxical worsening, local recurrence, and new bone destruction. After a high suspicion of mixed infection, a MAC-directed combination regimen (including azithromycin and a short course of amikacin) was added, leading to complete clinical cure; subsequent repeat cultures confirmed the presence of MAC. This is the first report of cutaneous MTB-MAC co-infection in the inguinal-perineal region of an adult without overt immune abnormalities, accompanied by disseminated bone lesions. This case highlights that in regions where nontuberculous mycobacteria (NTM) are co-endemic, atypical destructive skin lesions with paradoxical worsening despite initial response to anti-tuberculosis therapy should raise suspicion of MAC co-infection. The combination of mNGS and conventional culture facilitates identification of mixed infections and guides precision therapy, but mNGS results must be interpreted cautiously in the clinical context. Full article

Objectives: Assessment of the impact on survival of endometrial carcinoma according to the 2009 FIGO (International Federation of Gynecology and Obstetrics) classification and the new FIGO 2023 classification highlighting the worse prognosis of the aggressive subtypes. Methods: This multicenter retrospective study included 1181 patients with endometrial cancer. Comprehensive clinical, pathological and treatment-related variables were collected. Primary outcomes included overall survival assessed through five-year follow-ups. Statistical analysis included comparative tests, Kaplan–Meier survival estimation, Cox proportional hazards models and ROC curves analysis to review prognostic accuracy. Results: Aggressive endometrial carcinoma (n = 353) showed significant worse overall survival compared with non-aggressive cases (35.7 versus 60 months). A novel classification based on FIGO 2023 was developed, integrating histological aggressiveness into a different stage and combining early non-aggressive stages in only one stage. While FIGO 2009 and 2023 classifications showed prognostic value, the new model improved risk stratification, clearly distinguishing high-risk groups. Multivariate analysis identified aggressive subtype, stage, age, diabetes, myometrial invasion and lymphovascular invasion as independent predictors. Conclusions: Aggressive histological subtype in endometrial cancer should carry greater prognostic weight in terms of survival and clinical management. Our findings support a potential shift in the current paradigm for these relatively rare but high-risk cases. Full article

Background/Objectives: Multilocus Inherited Neoplasia Alleles Syndrome (MINAS) is defined by the presence of germline pathogenic or likely pathogenic variants in two or more distinct cancer susceptibility genes (CSGs) in the same individual. Although carriers may present more complex phenotypes, the clinical and molecular spectrum of MINAS remains poorly characterized, particularly in underrepresented populations. We aim to describe this phenomenon in a cohort of individuals from a Chilean institution. Methods: We retrospectively reviewed individuals evaluated at the Oncogenetic Counseling Unit of Fundación Arturo López Pérez (FALP) between 2020 and 2026 who underwent hereditary cancer multi-gene panel testing. Cases fulfilling MINAS criteria were described. We analyzed the association between MINAS and age at cancer diagnosis or multiple primary cancers, and reviewed reported cases with the same gene combinations. Results: From 1962 individuals tested, 398 harbored a pathogenic or likely pathogenic variant, and 14 fulfilled MINAS criteria, yielding a prevalence of 3.51% among positive cases. Breast cancer was the most common tumor type (76.9%), and ATM and CDKN2A were the most frequently involved genes. MINAS was significantly associated with a younger age at cancer diagnosis, but not with multiple primary cancers. Conclusions: MINAS prevalence in our cohort and the association with a younger diagnosis of cancer was consistent with published series. We identified seven previously unreported gene combinations, and common founder variants shifted the pattern away from predominantly BRCA-associated combinations. Despite the small sample size, this study adds relevant data from an underrepresented Latin American population. Full article

Palliative systemic therapy for advanced thymic epithelial tumors (TET) is challenging due to scarce evidence and biological heterogeneity. Scientific discussion, as in this review, is limited to cross-trial comparisons of small, non-randomized studies. Platinum-based combination chemotherapy is the standard first-line treatment. Here, we challenge the addition of anthracyclines to platinum in thymoma. We critically appraise new combinations with antiangiogenic drugs and immune checkpoint inhibitors (ICI) in thymic carcinoma and their implications for therapy sequences. We review the data on classic genomics-based targeted therapy that does not benefit most patients and explore established and upcoming biomarkers including KIT and HER2. Finally, we discuss investigational therapies like anti-TROP2 antibody–drug conjugates and proteasome inhibitors, as well as topics and design of future research. Full article